Asset Management, GIS and LiDAR Projects

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pavement design

Pavement management incorporates data collected utilizing various methods to gain a complete view of how the pavement is performing through its life-cycle. One of the most common practices in pavement inspection is imaging utilizing high-resolution cameras mounted on vehicles outfitted with precision GPS and inertial navigation. This imaging, when combined with laser profiling, constitutes a typical pavement inspection setup utilized by many DOTs as well as Local government agencies.

Pavement Inspections tend to follow a process that in many cases is proprietary and “black box” in nature. This makes it hard for the purchasing agency to see how their roads were inspected and how the resulting pavement condition scores were generated. Our team of Engineers and GIS professionals have worked hard to develop a process to remove the “black box” related pavement inspection and to make it easy and simple to trace inspection results back to their originating distresses from the field.

First, our entire process is geospatial in nature from the get-go. Our van’s location is tracked in six-dimensions in real-time and this information is used to calculate the exact location of pavement cracks in the resulting images. Next, the pavement images are geospatially referenced in 3-d and 1mm-pixel resolution, making it easy to extract low-severity cracks in a true 3-d environment. This process then allows us to create GIS vectors (points, lines and polygons) of each distress for each pavement image and deliver them to our clients as part of the pavement inspection deliverables.

This is a crucial piece to the pavement inspection “story” because it shows the purchasing agency exactly what distresses were identified and measured when creating the pavement condition scores for a section of road. Being able to see these distresses on a map helps to complete the story by providing the ability for a rigorous QA/QC process utilizing some simple GIS tools.

Each Section of road can be colored by the condition score and its range of values. This tells one component of its story. The underlying distress information tells the rest of the story related to “How” a section of road was scored and assigned its inspection score. By having this information at their fingertips, pavement inspection personnel have a GIS-centric and user-friendly tool that allows them to QA/QC pavement inspection data efficiently.

We just recently completed a cool project for an airport client who was having issues with their concrete surface and “pop-outs” caused by extended freeze/thaw weather events. Pop-outs are caused when the surface of the concrete sheds pieces that are about an inch wide and can be anywhere from <1cm to 3cm deep. The following graphic shows what the pop-outs look like in the field.

The client was looking for a way to quantify the number of pop-outs per slab using an automated process to avoid having to survey every pop-out which would prove to be cost-prohibitive based on the overall size of the project.

Earth Eye deployed 2 teams of data collection vehicles to compare the imagery that could be obtained from our right-of-way cameras as well as from our pavement camera.

The pavement camera has a resolution of 1mm and gives us the ability to resolve the pop-outs from a nadir view, making it easier to automate the extraction of these features from the imagery. Also, the nadir view gives us more spatial accuracy, so the locations of the pop-outs can be accurately mapped and then compared with future imagery to help quantify the amount of new pop-outs that have arisen since the last inventory. Furthermore, the gray-scale image provided by the pavement camera provided more contrast between the concrete surface and the pop-out which is much lighter in color. It was determined that the nadir-view pavement camera provided the best starting point, from which to test the automated pop-out extraction process. The following image illustrates a sample pavement image – note the pop-outs are very visible without having to zoom into the image.

The next image shows the results of our automated classification routine without any manual augmentation of missed pop-outs. We are realizing a consistent yield of greater than 95% of pop-outs identified as compared to control slabs that were collected manually in the field. Being able to efficiently map the pop-outs with a very high-yielding and automated algorithm allows us to efficiently map the pop-outs to support maintenance operations for this airport facility.

All of the pop-outs are geospatially referenced, so we can export all of the pop-outs as polygons with an area measurement associated with them. This area can then be converted to a severity and used to prescribe a specific maintenance activity based on the size and depth of the pop-out. The goal of the project was to create a quantified measurement (count) of the pop-outs for this entire project and we successfully completed this task with high-yielding, geospatial results.

We have been working with some automated methods for quantifying crack measurements and have had some interesting results. How great would it be to collect pavement images, batch them on a server and have it spit out accurate crack maps that you can overlay in a GIS? The technology is here! Or, is it?

Most pavement inspections involve intricate processes where pavement experts rate segments visually, either from field visits or rating pavement images in the office. This introduces a lot of subjectivity in the rating results and typically culminates in a spreadsheet showing pavement ratings by segment. The data is then modeled using ASTM performance curves that have been built from industry proven pavement experiments.

There is no doubt that these curves are tried and true representations of how pavement performs in varying physical and environmental conditions and each project should take these factors into consideration when developing the preservation plans for an agency.

We have been working to develop a rating workflow that focuses on a combination of automated and manual processes to bridge the current gap of Quantitative and Qualitative pavement inspections. The way we are doing this is through the application of GIS to the automated rating process. Here’s how it works…

First, we begin with a pavement image from our LRIS pavement imaging system. Images are captured at a 1mm-pixel resolution and then analyzed through an automated image processing workflow.

The resulting image creates a “crack map” that identifies the type, severity and extent of the distresses on that section of pavement. The process is fully automated and handled by the computer.

Once we have the crack maps in place, we then apply a manual editing process that is GIS-centric by nature and the resulting crack map is a more accurate representation of the real-world conditions.

Once the edited crack maps are compiled, the data is exported to a GIS where the extents are calculated geospatially and then integrated with a pavement management system. This is where all of the Pavement Condition Indices (PCI) are calculated and applied to each agency’s specific pavement rating methodologies. Since the process is geospatial in nature, it is easily imported to ANY pavement management software and gives our clients the flexibility to apply any rating methodology they desire.

Of course, all agencies have a certain spending threshold and there are cases where automation is the only way to cost-effectively manage large volumes of data. We recognize this fact and are working hard to bridge the gap of available funding and high quality data.

DTS/EarthEye just completed a 9-mile mobile LiDAR scan of I-95 here in Florida and provided one of our partners with cross-slope information in a period of days. The data was collected with our buddies at Riegl USA using their VMX-250 mobile LiDAR. This information will be used to generate pavement resurfacing plans for the Florida Department of Transportation (FDOT).

This project shows the value that this type of project can provide to the end user on both sides of the fence.

First, the paving contractor can use this data to develop their 30% plans for submittal to FDOT when bidding on a resurfacing or re-design contract. Having accurate and relevant data related to the roadway’s characteristics gives the paving contractor an edge over the competition because they know what the field conditions are before preparing an over-engineered design specification. This happens all of the time because the detailed field conditions are unknown while they are preparing their plans and they only have historical information to work from.

On the other side of the fence resides the FDOT. They can benefit from this information because if they can provide this detailed information as part of a bid package, they can reap the benefits that are gained from better information. If all contractors have the detailed as-built information (or in this case, accurate cross-slopes), they can all prepare their submittals using the same base information. This will provide the FDOT project manager with more accurate responses based on true field conditions, resulting in more aggressive pricing and decreased project costs.

Here are some screenshots of the information.

LiDAR Data Viewed by Intensity and Corresponding Cross-Slope Profile

Once the data has been collected and calibrated, we generate cross-slopes at a defined interval and export those out as 3D vectors.

These vectors are then symbolized based on their cross-slope percentages and exported as a KML file for ease of use.

Although this is a pretty simple step, the presentation of the data in Google Earth makes it easy for the end-user to visually identify problem areas and design the corrective actions according to field measurements.

We just completed a mobile LiDAR project that was designed to support a roadway resurfacing project in Orlando. The project was centered on the use of mobile LiDAR to generate roadway profile data that Engineers could use to design a resurfacing project. Obviously the data would need to be accurate and we were able to hit the mark and best of all – prove it!

Overview of SR417 Project

We collected the data using the new Riegl VMX-250 mobile LiDAR unit using a single pass in the north and southbound directions. We only required one pass in each direction to collect the road data which makes it very efficient from the data collection standpoint. In the past, we had to collect “strips” of data and then “sew” them all together during the calibration process. In this case, we took the opposing (NB and SB) strips and calibrated them relative to one another and then they are brought down to control as a final step.

LiDAR Coverage by Flight Line

Most of our clients are interested in the overall accuracies of the data, so we have built accuracy assessment tools that make it easy to review the LiDAR against survey control. The tool is simple to use and allows us to sort the results and dig deeper into the least accurate points to see why there might be discrepancies in the control vs the TIN surface.

417 Accuracy Control Report

For this project, we achieved an RMSE of .0525 ft – calculated by comparing the control elevations (Z) against the TIN elevations (Z TIN). This is important because we can check the point cloud against known control that was collected throughout the project and provide detailed information about the accuracy of the data.

Once the data has been calibrated sufficiently, we can then generate all of the derivative products for this project. We generated the following data for our roadway engineers:

Pavement Cross-Slope

Shoulder Cross-Slope

3D Roadway Markings

Edge of Friction Course

3D Vector Data

This data set also supports detailed engineering analysis related to guardrail height above the roadway. This is an important factor to consider because there are specific standards that define where the guardrail is placed, more specifically, its height above the roadway, to corral vehicles that end up impacting the guardrail in an accident situation. The following graphic displays how this measurement can be made in the point cloud data.

We’ve wrung out the calibration issues with our mobile LiDAR and we have a solid solution that we can hang our hat on. We have achieved RTK-equivalent accuracies for short runs of less than 5 miles and today, we’re heading out to see how well the accuracies hold up for a 30-mile run.

No worries about having this ready for prime time today – most projects we’re dealing with are only 3-5 miles right now. Our goal is to figure out the best way to keep the GPS solution in check for long runs to avoid post-processing and calibration issues on the back-end of the collect. Since most of the required measurements are “relative”, the LiDAR data is good, but we are striving to crack the “absolute” accuracy nut – and that involves a solid calibration of the equipment. We’ve always known this, but actually “doing” that is a different story!

This is a 5-mile run that we collected here locally. The data has been filtered to a bare pavement surface and we have run cross-sections every 5 feet. Each cross-section can be exported to CAD, GIS, Microstation, etc and used to build pavement resurfacing design drawings.

We have been able to make the processing of this data “semi-automated”. Basically, we have to draw in the breaklines which typically correspond to the pavement stripes. These define the lanes of travel and then we do a slope calculation (relative measurement) from one breakline to another – effectively calculating the cross-slope percentage for each lane. We’re also exporting out a tabular format so clients can use that information to verify the values against a pavement design spec.

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Slope 1 refers to the left lane (southbound) and Slope 2 refers to the right lane of travel (northbound) and all slopes are percentages.